scholarly journals Progress of the China mammal diversity observation network (China BON-Mammal) based on camera-trapping

2020 ◽  
Vol 28 (9) ◽  
pp. 1115-1124
Author(s):  
Yaqiong Wan ◽  
Jiaqi Li ◽  
Xingwen Yang ◽  
Sheng Li ◽  
Haigen Xu ◽  
...  
2017 ◽  
Vol 25 (3) ◽  
pp. 237-245 ◽  
Author(s):  
Zhishu Xiao ◽  
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...  

2020 ◽  
Vol 287 (1918) ◽  
pp. 20192353 ◽  
Author(s):  
Kevin Leempoel ◽  
Trevor Hebert ◽  
Elizabeth A. Hadly

Before environmental DNA (eDNA) can establish itself as a robust tool for biodiversity monitoring, comparison with existing approaches is necessary, yet is lacking for terrestrial mammals. Moreover, much is unknown regarding the nature, spread and persistence of DNA shed by animals into terrestrial environments, or the optimal experimental design for understanding these potential biases. To address some of these challenges, we compared the detection of terrestrial mammals using eDNA analysis of soil samples against confirmed species observations from a long-term (approx. 9-year) camera-trapping study. At the same time, we considered multiple experimental parameters, including two sampling designs, two DNA extraction kits and two metabarcodes of different sizes. All mammals regularly recorded with cameras were detected in eDNA. In addition, eDNA reported many unrecorded small mammals whose presence in the study area is otherwise documented. A long metabarcode (≈220 bp) offering a high taxonomic resolution, achieved a similar efficiency as a shorter one (≈70 bp) and a phosphate buffer-based extraction gave similar results as a total DNA extraction method, for a fraction of the price. Our results support that eDNA-based monitoring should become a valuable part of ecosystem surveys, yet mitochondrial reference databases need to be enriched first.


2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Jose Antonio Moreira Lima

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).


2020 ◽  
Vol 117 ◽  
pp. 106565
Author(s):  
Roxana Triguero-Ocaña ◽  
Joaquín Vicente ◽  
Pablo Palencia ◽  
Eduardo Laguna ◽  
Pelayo Acevedo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefano Anile ◽  
Sébastien Devillard

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


2021 ◽  
Author(s):  
José Luis Mena ◽  
Hiromi Yagui ◽  
Vania Tejeda ◽  
Emilio Bonifaz ◽  
Eva Bellemain ◽  
...  

Author(s):  
Yong Xiao ◽  
Yonggang Zeng ◽  
Yun Zhao ◽  
Yuxin Lu ◽  
Weibin Lin

The traditional distribution network lacks real-time topology information, which makes the implementation of smart grid complicated. The smart grid needs to monitor and dispatch the grid to maintain the economic and safe operation of the system. In this paper, we propose a topology detection algorithm of the distribution network based on adaptive state observer. Based on the transient dynamic model of the distribution network, the line states of the distribution network are regarded as unknown parameters, a virtual adaptive state observation network is built, and the topology can be inferred by the changes of adaptive state parameters. Finally, the effectiveness of our algorithm is verified by the MATLAB simulation experiments.


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